Kristian Gonzalez Barman

Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized as an ability to perform certain tasks. We extend this notion of scientific understanding by considering a set of questions that gauge different levels of scientific understanding, covering information retrieval, the capability to arrange information to produce an explanation, and the ability to infer how things would be different under different circumstances. We suggest building a Scientific Understanding Benchmark (SUB), formed by a set of these tests, allowing for the evaluation and comparison of scientific understanding. Benchmarking plays a crucial role in establishing trust, ensuring quality control, and providing a basis for performance evaluation. By aligning machine and human scientific understanding we can improve their utility, ultimately advancing scientific understanding and helping to discover new insights within machines.

There are several theses in political science that are usually explicitly called ‘laws’. Other theses are generally thought of as laws, but often without being explicitly labelled as such. Still other claims are well-supported and arguably interesting, while no one would be tempted to call them laws. This situation raises philosophical questions: which theses deserve to be called laws and which not? And how should we decide about this? In this paper we develop and motivate a strategy for thinking about laws in political science which integrates two core concepts: spatio-temporal stability and social mechanisms. The proposed strategy is a set of guidelines that political scientists can use to reflect on and argue about specific cases within their discipline, not a clear-cut demarcation criterion. We defend and motivate this strategy and apply it to two cases (one with respect to state repression, one about parliamentary elections). After we have developed and motivated our strategy for political science, we show that our proposal is relevant for other disciplines in the social sciences. We explain how our views fit into critical realism and embed them in the debate on laws in general philosophy of science and in the philosophy of the social sciences.

The main aim of this paper is to evaluate the evolution of Accident Causation Models (ACMs) from the perspective of philosophy of science. I use insights from philosophy of science to provide an epistemological analysis of the ways in which engineering scientists judge the value of different types of ACMs and to offer normative reflection on these judgements. I review three widespread ACMs and clarify their epistemic value: sequential models, epidemiological models, and systemic models. I first consider how they produce and ensure safety (‘usefulness’) relative to each other. This is evaluated in terms of the ability of models to afford a larger set of relevant counterfactual inferences. I take relevant inferences to be ones that provide safety (re)design information or suggest countermeasures (safety-design-interventions). I argue that systemic models are superior at providing said safety information. They achieve this, in part, by representing non-linear causal relationships. The second issue is whether we should retire linear and epidemiological models. I argue negatively. If the goal is to assign blame, linear models are better candidates. The reason is that they can provide semantic simplicity. Similarly, epidemiological models are better suited for the goal of audience communication because they can provide cognitive salience.

This paper outlines a procedure for assessing the quality of failure explanations in engineering failure analysis. The procedure structures the information contained in explanations such that it enables to find weak points, to compare competing explanations, and to provide redesign recommendations. These features make the procedure a good asset for critical reflection on some areas of the engineering practice of failure analysis and redesign. The procedure structures relevant information contained in an explanation by means of structural equations so as to make the relations between key elements more salient. Once structured, the information is examined on its potential to track counterfactual dependencies by offering answers to relevant what-if-things-had-been-different questions. This criterion for explanatory goodness derives from the philosophy of science literature on scientific explanation. The procedure is illustrated by applying it to two case studies, one on Failure Analysis in Mechanical Engineering (a broken vehicle shaft) and one on Failure Analysis in Civil Engineering (a collapse in a convention center). The procedure offers failure analysts a practical tool for critical reflection on some areas of their practice while offering a deeper understanding of the workings of failure analysis (framing it as an explanatory practice). It, therefore, allows to improve certain aspects of the explanatory practices of failure analysis and redesign, but it also offers a theoretical perspective that can clarify important features of these practices. Given the programmatic nature of the procedure and its object (assessing and refining explanations), it extends work on the domain of computational argumentation.

We argue that two well-known examples (strawberry distribution and Konigsberg bridges) generally considered genuine cases of distinctively _mathematical_ explanation can also be understood as cases of distinctively _generic_ explanation. The latter answer resemblance questions (e.g., why did neither person A nor B manage to cross all bridges) by appealing to ‘generic task laws’ instead of mathematical necessity (as is done in distinctively mathematical explanations). We submit that distinctively generic explanations derive their explanatory force from their role in ontological unification. Additionally, we argue that distinctively generic explanations are better seen as standardly mathematical instead of distinctively mathematical. Finally, we compare and contrast our proposal with the work of Christopher Pincock on abstract explanations in science and the views of Michael Strevens on abstract causal event explanations.

The periodic table is one of the best-known systems of classification in science. Because of the information it contains, it raises explanation-seeking questions. Quantum mechanical models of the behaviour of electrons may be seen as providing explanations in response to these questions. In this paper we first address the question ‘Do quantum mechanical models of atoms provide legitimate explanations?’ Because our answer is positive, our next question is ‘Are the explanations provided by quantum mechanical models of atoms mechanistic explanations?’. This question is motivated by the fact that in many scientific disciplines, mechanistic explanations are abundant. Because our answer to the second question is negative, our last question is ‘What kind of explanation do quantum mechanical models of atom provide?’ By addressing these questions, we shed light on the nature of an important type of chemical explanation.

This paper discusses a class of mechanistic explanations employed in engineering science where the activities and organization of nonstandard entities are cited as core factors responsible for failures. Given the use of mechanistic language by engineers and the manifestly mechanistic structure of these explanations, I consider several interpretations of these explanations within the new mechanical framework. I argue that these interpretations fail to solve several philosophical problems and propose an account of fictional mechanism explanations instead. According to this account, fictional mechanism explanations provide descriptions of fictional mechanisms that enable the tracking of counterfactual dependencies of the physical system they model by capturing system constraints. Engineers use these models to learn about and understand properties of materials, to build computational simulations of their behaviour, and to design new materials.

In this paper we investigate how inference to the best explanation (IBE) works in engineering science, focussing on the context of malfunction explanation. While IBE has gotten a lot of attention in the philosophy of science literature, few, if any, philosophical work has focussed on IBE in engineering science practice. We first show that IBE in engineering science has a similar structure as IBE in other scientific domains in the sense that in both settings IBE hinges on the weighing of explanatory virtues. We then proceed to show that, due to the intimate connection between explanation and redesign in engineering science, there is a further engineering domain-specific virtue in terms of which engineering malfunction explanations are evaluated, viz. the virtue of redesign utility. This virtue entails that the explanatory information offered by a malfunction explanation should be instrumental in predicting counterfactual dependencies in redesigned systems. We illustrate and elaborate these points in terms of a number of engineering examples, focussing in particular on the 2009 crash of Air France Flight 447. Our extension of analyses of IBE and explanation to engineering science practice offers new insights by identifying a new explanatory virtue in malfunction explanation: redesign utility.